Eye tracking evidence for the reinstatement of emotionally negative and neutral memories.

Recent eye tracking studies have linked gaze reinstatement-when eye movements from encoding are reinstated during retrieval-with memory performance. In this study, we investigated whether gaze reinstatement is influenced by the affective salience of information stored in memory, using an adaptation of the emotion-induced memory trade-off paradigm. Participants learned word-scene pairs, where scenes were composed of negative or neutral objects located on the left or right side of neutral backgrounds. This allowed us to measure gaze reinstatement during scene memory tests based on whether people looked at the side of the screen where the object had been located. Across two experiments, we behaviorally replicated the emotion-induced memory trade-off effect, in that negative object memory was better than neutral object memory at the expense of background memory. Furthermore, we found evidence that gaze reinstatement was related to recognition memory for the object and background scene components. This effect was generally comparable for negative and neutral memories, although the effects of valence varied somewhat between the two experiments. Together, these findings suggest that gaze reinstatement occurs independently of the processes contributing to the emotion-induced memory trade-off effect.

RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI.

Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.

The "Narratives" fMRI dataset for evaluating models of naturalistic language comprehension.

The "Narratives" collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

BrainIAK: The Brain Imaging Analysis Kit.

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be se amlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

Driving high-functioning clinical teams: An advanced practice registered nurse and physician assistant optimization initiative.

BACKGROUND: Rapid changes in the health care marketplace are driving health care systems to modify operations by which the advanced practice registered nurse (APRN) and physician assistant (PA) clinicians serve patients. By identifying more effective and efficient utilization workflows, organizations can meet these demands resulting in high-functioning clinical teams. LOCAL PROBLEM: With the growing number of APRNs and PAs within a large academic medical center, there was a recognized need to establish effective and efficient utilization practices for these health care providers. METHODS: Directors of the Advanced Practice Provider Best Practice Center developed an internal nurse practitioner (NP)/certified nurse midwife (CNM)/clinical nurse specialist (CNS)/PA assessment service in which evaluations were conducted to optimize APRN and PA practice at the clinical/department level. This assessment excluded certified registered nurse anesthetists. INTERVENTIONS: Thirty-two clinical areas were evaluated between September 2016 and May 2019. This included an NP/CNM/CNS/PA survey and over 200 individual NP/CNM/CNS and PA provider interviews. Assessments addressed utilization, billing practices, professional development, and communication among team members. RESULTS: Qualitative and quantitative reports were compiled. Many common themes were identified. These were broken down into three major categories: productivity, clinical operations, and professional development/support. CONCLUSIONS: Several recommendations were presented to department leaders regarding NP/CNM/CNS/PA practice. Those departments who implemented several of the recommendations showed positive outcomes. This was evidenced by increased financial gain (increased relative value units, increase in revenue generated), increased patient access (increased clinic densities), and overall NP/CNM/CNS/PA satisfaction.

Driving high-functioning clinical teams: An advanced practice registered nurse and PA optimization initiative.

BACKGROUND: Rapid changes in the healthcare marketplace are driving healthcare systems to modify operations by which advanced practice registered nurses (APRNs) and physician assistants (PAs) serve patients. By identifying more effective and efficient utilization workflows, organizations can meet these demands, resulting in high-functioning clinical teams. LOCAL PROBLEM: With the growing number of APRNs and PAs in a large academic medical center, there was a recognized need to establish effective and efficient utilization practices for these healthcare providers. METHODS: Directors of the APP Best Practice Center developed an internal NP/certified nurse midwife (CNM)/clinical nurse specialist (CNS)/PA assessment service which conducted evaluations to optimize APRN and PA practice at the clinical/department level. This assessment excluded certified registered nurse anesthetists. INTERVENTIONS: Thirty-two clinical areas were evaluated between September 2016 and May 2019. This included an NP/CNM/CNS/PA survey and more than 200 individual NP/CNM/CNS/PA interviews. Assessments addressed utilization, billing practices, professional development, and communication among team members. RESULTS: Qualitative and quantitative reports were compiled. Many common themes were identified. These were broken down into three major categories: productivity, clinical operations, and professional development/support. CONCLUSIONS: Several recommendations were presented to department leaders about NP/CNM/CNS/PA practice. Departments that implemented several of the recommendations showed positive outcomes. This was evidenced by increased financial gain (increased relative value units, increase in revenue generated), increased patient access (increased clinic densities), and overall NP/CNM/CNS/PA satisfaction.

Targeted Memory Reactivation during Sleep Elicits Neural Signals Related to Learning Content.

Retrieval of learning-related neural activity patterns is thought to drive memory stabilization. However, finding reliable, noninvasive, content-specific indicators of memory retrieval remains a central challenge. Here, we attempted to decode the content of retrieved memories in the EEG during sleep. During encoding, male and female human subjects learned to associate spatial locations of visual objects with left- or right-hand movements, and each object was accompanied by an inherently related sound. During subsequent slow-wave sleep within an afternoon nap, we presented half of the sound cues that were associated (during wake) with left- and right-hand movements before bringing subjects back for a final postnap test. We trained a classifier on sleep EEG data (focusing on lateralized EEG features that discriminated left- vs right-sided trials during wake) to predict learning content when we cued the memories during sleep. Discrimination performance was significantly above chance and predicted subsequent memory, supporting the idea that retrieval leads to memory stabilization. Moreover, these lateralized signals increased with postcue sleep spindle power, demonstrating that retrieval has a strong relationship with spindles. These results show that lateralized activity related to individual memories can be decoded from sleep EEG, providing an effective indicator of offline retrieval.SIGNIFICANCE STATEMENT Memories are thought to be retrieved during sleep, leading to their long-term stabilization. However, there has been relatively little work in humans linking neural measures of retrieval of individual memories during sleep to subsequent memory performance. This work leverages the prominent electrophysiological signal triggered by lateralized movements to robustly demonstrate the retrieval of specific cued memories during sleep. Moreover, these signals predict subsequent memory and are correlated with sleep spindles, neural oscillations that have previously been implicated in memory stabilization. Together, these findings link memory retrieval to stabilization and provide a powerful tool for investigating memory in a wide range of learning contexts and human populations.

Demonstrating advanced practice provider value: Implementing a new advanced practice provider billing algorithm.

BACKGROUND: Rapid changes in health care are driving the adjustment of work flow by which providers serve patients in team-based care. Specifically, there is a need to develop more effective and efficient utilization with accurate attribution of advanced practice providers' (APPs) productivity. LOCAL PROBLEM: The Directors of the APP-Best Practice Center conducted assessments of each clinical area at MUSC Health, a large academic medical center. A knowledge gap was identified, not only regarding billing practices of the APPs (nurse practitioners/physician assistants) but also in the utilization of APPs to practice to the fullest extent of their license, education, and experience. METHODS: By substantiating APPs' contribution margin through the process of implementing a new standardized APP billing algorithm, a change in practice was accepted by senior leadership and a new APP billing algorithm was built while following updated practice laws, compliance/legal standards, and hospital bylaws/regulations. INTERVENTIONS: A new billing algorithm was implemented on July 1, 2017, and outcomes were evaluated 12 months after implementation. RESULTS: This project uncovered the work already performed by APPs while increasing relative value units, collections, and overall patient encounters by the APP/physician team. Findings suggest improved utilization and appropriate attribution of productivity. CONCLUSIONS: With the APP work force growing, the implementation of electronic medical record systems, and today's health care financial constraints, it is imperative that health care systems standardize their billing practices. The APP billing algorithm is a critical tool that will help to meet this demand.

Demonstrating advanced practice provider value: Implementing a new advanced practice provider billing algorithm.

BACKGROUND: Rapid changes in healthcare are driving the adjustment of work flow by which providers serve patients in team-based care. Specifically, there is a need to develop more effective and efficient utilization with accurate attribution of advanced practice providers' (APPs) productivity. LOCAL PROBLEM: The directors of the APP-Best Practice Center conducted assessments of each clinical area at the Medical University of South Carolina (MUSC) Health, a large academic medical center. A knowledge gap was identified, not only regarding billing practices of the APPs (NPs and physician assistants) but also in the use of APPs to practice to the fullest extent of their license, education, and experience. METHODS: By substantiating APPs' contribution margin through the process of implementing a new standardized APP billing algorithm, a change in practice was accepted by senior leadership and a new APP billing algorithm was built that follows updated practice laws, compliance/legal standards, and hospital bylaws and regulations. INTERVENTIONS: A new billing algorithm was implemented on July 1, 2017, and outcomes were evaluated 12 months after implementation. RESULTS: This project uncovered the work already performed by APPs while increasing relative value units, collections, and overall patient encounters by the APP/physician team. Findings suggest improved utilization and appropriate attribution of productivity. CONCLUSIONS: With the APP workforce growing, the implementation of electronic medical record systems, and today's healthcare financial constraints, healthcare systems must standardize their billing practices. The APP billing algorithm is a critical tool that will help to meet this demand.

Reductions in Retrieval Competition Predict the Benefit of Repeated Testing.

Repeated testing leads to improved long-term memory retention compared to repeated study, but the mechanism underlying this improvement remains controversial. In this work, we test the hypothesis that retrieval practice benefits subsequent recall by reducing competition from related memories. This hypothesis implies that the degree of reduction in competition between retrieval practice attempts should predict subsequent memory for practiced items. To test this prediction, we collected electroencephalography (EEG) data across two sessions. In the first session, participants practiced selectively retrieving exemplars from superordinate semantic categories (high competition), as well as retrieving the names of the superordinate categories from exemplars (low competition). In the second session, participants repeatedly studied and were tested on Swahili-English vocabulary. One week after session two, participants were again tested on the vocabulary. We trained a within-subject classifier on the data from session one to distinguish high and low competition states. We then used this classifier to measure the change in competition across multiple successful retrieval practice attempts in the second session. The degree to which competition decreased for a given vocabulary word predicted whether it was subsequently remembered in the third session. These results are consistent with the hypothesis that repeated testing improves retention by reducing competition.

Competitive learning modulates memory consolidation during sleep.

Competition between memories can cause weakening of those memories. Here we investigated memory competition during sleep in human participants by presenting auditory cues that had been linked to two distinct picture-location pairs during wake. We manipulated competition during learning by requiring participants to rehearse picture-location pairs associated with the same sound either competitively (choosing to rehearse one over the other, leading to greater competition) or separately; we hypothesized that greater competition during learning would lead to greater competition when memories were cued during sleep. With separate-pair learning, we found that cueing benefited spatial retention. With competitive-pair learning, no benefit of cueing was observed on retention, but cueing impaired retention of well-learned pairs (where we expected strong competition). During sleep, post-cue beta power (16-30 Hz) indexed competition and predicted forgetting, whereas sigma power (11-16 Hz) predicted subsequent retention. Taken together, these findings show that competition between memories during learning can modulate how they are consolidated during sleep.

Awareness of cord blood collection and the impact on banking.

BACKGROUND: Umbilical cord blood (UCB) is an important source of hematopoietic stem cells for transplantation especially in minority populations with limited chances of finding a histocompatible volunteer donor in the registry. UCB has the advantages of early availability, successful outcomes despite some histocompatibility mismatch, and low incidence of chronic graft-versus-host disease. Public cord blood banks that disseminate UCB products for transplant depend on voluntary donation at participating hospitals and obstetrical providers for collection. PROCEDURE: Using survey questionnaires, we evaluated attitudes toward UCB donation, the frequency of donation, and provider opinions on UCB collection in the greater St. Louis metropolitan area that caters to minority ethnicities in significant numbers. RESULTS: Our data suggest that nervousness and lack of information regarding the donation and utility of the product were ubiquitous reasons for not donating. Additionally, irrespective of age or level of education, women relied on healthcare providers for information regarding UCB donation. Providers reported primarily time constraints to discussing UCB donation at prenatal visits (54%). Of the interviewees, 62% donated UCB. Fallout due to refusal or preferring private banking was miniscule. CONCLUSIONS: These results suggest that dedicated personnel focused on disseminating information, obtaining consent, and collecting the UCB product at major hospitals can enrich cord blood banks especially with minority cords. Sustained and focused efforts could improve upon a relatively high wastage rate and ensure a robust supply of UCB products at local public banks.

Engineering Enhanced Vaccine Cell Lines To Eradicate Vaccine-Preventable Diseases: the Polio End Game.

UNLABELLED: Vaccine manufacturing costs prevent a significant portion of the world's population from accessing protection from vaccine-preventable diseases. To enhance vaccine production at reduced costs, a genome-wide RNA interference (RNAi) screen was performed to identify gene knockdown events that enhanced poliovirus replication. Primary screen hits were validated in a Vero vaccine manufacturing cell line using attenuated and wild-type poliovirus strains. Multiple single and dual gene silencing events increased poliovirus titers >20-fold and >50-fold, respectively. Host gene knockdown events did not affect virus antigenicity, and clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9-mediated knockout of the top candidates dramatically improved viral vaccine strain production. Interestingly, silencing of several genes that enhanced poliovirus replication also enhanced replication of enterovirus 71, a clinically relevant virus to which vaccines are being targeted. The discovery that host gene modulation can markedly increase virus vaccine production dramatically alters mammalian cell-based vaccine manufacturing possibilities and should facilitate polio eradication using the inactivated poliovirus vaccine. IMPORTANCE: Using a genome-wide RNAi screen, a collection of host virus resistance genes was identified that, upon silencing, increased poliovirus and enterovirus 71 production by from 10-fold to >50-fold in a Vero vaccine manufacturing cell line. This report provides novel insights into enterovirus-host interactions and describes an approach to developing the next generation of vaccine manufacturing through engineered vaccine cell lines. The results show that specific gene silencing and knockout events can enhance viral titers of both attenuated (Sabin strain) and wild-type polioviruses, a finding that should greatly facilitate global implementation of inactivated polio vaccine as well as further reduce costs for live-attenuated oral polio vaccines. This work describes a platform-enabling technology applicable to most vaccine-preventable diseases.

Robotic-Assisted Thoracic Surgery for Early-Stage Lung Cancer: A Review.

This review evaluates the benefits and disadvantages associated with the use of robotic-assisted technology in performing lobectomies in patients with early-stage lung cancer. The author conducted a literature search of Ovid®, MEDLINE®, PubMed®, and CINAHL® for articles published from 2005 to 2013. Search criteria included key terms such as robot, robotic, robotic-assisted lobectomy, and lung cancer. Of 922 articles, the author included a total of 12 research-based published studies in the analysis and incorporated the findings into an evidence table. Results showed that robotic-assisted lobectomies are feasible safe procedures for patients with stage 1A or 1B lung cancer; however, there is a steep learning curve and long-term randomized studies evaluating robotic-assisted lobectomy and conventional posterolateral thoracotomy or video-assisted thoracic lobectomy are needed. For patient safety, perioperative nurses should be aware of the length of time and experience required to perform these procedures, the costs, techniques, benefits, and disadvantages.

Developing a strategy to identify and treat older patients with postoperative delirium.

Postoperative delirium is one of the most common adverse outcomes in elderly patients undergoing surgery and is associated with increased morbidity, length of stay, and patient care costs. The purpose of this quality improvement project was to evaluate the effectiveness of a multicomponent strategy to identify and treat general surgical patients 65 years of age or older at risk for and who develop postoperative delirium at Cape Cod Hospital, a community hospital in southern New England. We evaluated 96 patients using the Mini-Cog assessment tool preoperatively and the Confusion Assessment Method (CAM) delirium screening tool or CAM-Intensive Care Unit (CAM-ICU) assessment tool postoperatively. Patients who tested positive during preoperative assessment underwent a postoperative delirium management protocol. We summarized data using descriptive statistics. The results showed an association between compliance and outcomes. High compliance with implementation of CAM and CAM-ICU assessment tools resulted in increased identification of postoperative delirium in the older surgical population. The use of screening tools helped facilitate early identification of postoperative delirium in elderly surgical patients.

Developing a robotic program in thoracic surgery at Cape Cod Hospital.

This study aimed to elucidate the stepwise progression of robotic skills required, the natural progression from thoracoscopic to robotic technique with a focus on approaching the hilar structures, and the nuances of perioperative care, in a community hospital and solo surgeon practice. It was a case-control analysis comparing 22 consecutive robotic-assisted lobectomy (RAL) procedures with 22 matched historic video-assisted lobectomy (VAL) procedures from June 2011 to December 2012. Patients undergoing VAL had 1.09 days greater length of stay than did patients who underwent RAL. Although hospital charges were significantly higher for RAL patients, patients undergoing VAL had greater blood loss, required longer hospital stay, had greater readmission rates, and were transferred more often to a rehabilitation facility at discharge. Subjectively, dexterity, ergonomics, and optics with the RALs were superior to the VALs. Favorable outcomes were demonstrated for patients undergoing RALs. Effective communication is paramount for the skill set and team building for a safe transition to practice.

Identification of Host Kinase Genes Required for Influenza Virus Replication and the Regulatory Role of MicroRNAs.

Human protein kinases (HPKs) have profound effects on cellular responses. To better understand the role of HPKs and the signaling networks that influence influenza virus replication, a small interfering RNA (siRNA) screen of 720 HPKs was performed. From the screen, 17 HPKs (NPR2, MAP3K1, DYRK3, EPHA6, TPK1, PDK2, EXOSC10, NEK8, PLK4, SGK3, NEK3, PANK4, ITPKB, CDC2L5 (CDK13), CALM2, PKN3, and HK2) were validated as essential for A/WSN/33 influenza virus replication, and 6 HPKs (CDK13, HK2, NEK8, PANK4, PLK4 and SGK3) were identified as vital for both A/WSN/33 and A/New Caledonia/20/99 influenza virus replication. These HPKs were found to affect multiple host pathways and regulated by miRNAs induced during infection. Using a panel of miRNA agonists and antagonists, miR-149* was found to regulate NEK8 expression, miR-548d-3p was found to regulate MAPK1 transcript expression, and miRs -1228 and -138 to regulate CDK13 expression. Up-regulation of miR-34c induced PLK4 transcript and protein expression and enhanced influenza virus replication, while miR-34c inhibition reduced viral replication. These findings identify HPKs important for influenza viral replication and show the miRNAs that govern their expression.

Targeting organic anion transporter 3 with probenecid as a novel anti-influenza a virus strategy.

Influenza A virus infection is a major global health concern causing significant mortality, morbidity, and economic loss. Antiviral chemotherapeutics that target influenza A virus are available; however, rapid emergence of drug-resistant strains has been reported. Consequently, there is a burgeoning need to identify novel anti-influenza A drugs, particularly those that target host gene products required for virus replication, to reduce the likelihood of drug resistance. In this study, a small interfering RNA (siRNA) screen was performed to identify host druggable gene targets for anti-influenza A virus therapy. The host organic anion transporter-3 gene (OAT3), a member of the SLC22 family of transporters, was validated as being required to support influenza A virus replication. Probenecid, a prototypical uricosuric agent and chemical inhibitor of organic anion transporters known to target OAT3, was shown to be effective in limiting influenza A virus infection in vitro (50% inhibitory concentration [IC(50)] of 5.0 × 10(-5) to 5.0 × 10(-4) µM; P < 0.005) and in vivo (P < 0.05). Probenecid is widely used for treatment of gout and related hyperuricemic disorders, has been extensively studied for pharmacokinetics and safety, and represents an excellent candidate for drug repositioning as a novel anti-influenza A chemotherapeutic.